Research On The Deployment Of Edge Computing Nodes In Cellular Internet Of Things

The cellular Internet of Things is the application expansion of the Internet and one of the important components of the new generation of communication technology.The Internet of Things,the mobile Internet,and the traditional Internet generate massive amounts of data every day to meet the needs of different types of network services.The rapid development of the network requires the support of lower latency and higher reliability connections.The proposal of edge computing has provided a good boost to the development of the network,but the deployment of edge computing nodes will also cause corresponding deployment costs.Therefore,how to deploy edge computing nodes more efficiently and reduce deployment costs while ensuring low-latency and high-reliability connections on the network and providing diversified services is one of the key issues that need to be resolved urgently.In response to the above problems,this article mainly conducts research work from the following three aspects:(1)Aiming at the problem of data sources in the cellular Internet of Things communication scenario,a simulation platform combining Network Simulator-3(NS-3)and Urban Traffic Simulation(SUMO)was built.On the one hand,according to the types of user nodes in the cellular Internet of Things,SUMO is used to generate user node types with different needs,and generate corresponding network communication scenarios and user movement trajectory data.On the other hand,using the NS-3 simulation platform,a network communication scenario based on the Long Term Evolution(LTE)module is established,and corresponding communication parameters are configured for various user nodes,so that the network can communicate normally.The simulation results show that the platform can perfectly simulate the communication process in the Internet of Things scene and generate corresponding communication data.(2)Aiming at the problem of determining the user data hotspot area in the cellular Internet of Things,a user hotspot area location algorithm based on the received signal strength is proposed.First,collect the Signal to Interference plus Noise Ratio(SINR)value according to the established communication simulation platform.Then,based on the Back Propagation(BP)neural network model,neural network training is performed on the relationship between the user's location area and the received signal strength from the positioning node,and the regional positioning is obtained according to the proposed training requirements model.Finally,use the model to determine the distribution of user hotspot data in the network scenario.The simulation results show that the neural network can be well trained to obtain the user area positioning model,and can obtain user hotspot areas.(3)Aiming at the problem of optimal deployment of edge computing nodes in the cellular Internet of Things,an edge computing node deployment scheme based on user hotspot areas is proposed.First,determine the communication hotspot area determined by the neural network positioning model obtained by training,determine the area where edge computing nodes need to be deployed and the number of edge computing nodes required.Then,using the improved simulated annealing algorithm,the conditions for deploying edge computing nodes and the constraints to be met for deploying edge computing nodes are analyzed,and an edge computing node deployment optimization model is established.Finally,the improved simulated annealing algorithm is used to obtain an optimized edge computing node deployment plan.The simulation results show that this method can minimize the deployment cost in the edge computing node deployment problem,and realizes the efficient deployment of edge computing nodes faster than other optimization algorithms.This article has 29 figures,6 tables,and 81 references.